Abstract
Clustering evolutionary data (or called evolutionary clustering) has received an enormous amount of attention in recent years. A recent framework (called temporal smoothness) considers that the clustering result should depend mainly on the current data while simultaneously not deviate too much from previous ones. In this paper, evolutionary data is clustered by a multi-objective evolutionary algorithm based on r-dominance, and the corresponding algorithm is named rEvoC. The rEvoC considers the previous clustering result (or historical data) as the reference point. We propose three strategies to define the reference point and to calculate the distance between a reference point and an individual. Based on the reference point and the r-dominance relation, the search could be guided into the region, in which a solution not only could cluster the current data well, but also does not shift two much from the previous one. Additionally, the rEvoC adopts one step k-means as a local search operator to accelerate the evolutionary search. Experimental results on two different data sets are given. The experimental results demonstrate that, the rEvoC achieves better performance than the corresponding static clustering algorithm and the evolutionary k-means algorithm.
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Chakrabarti, D., Kumar, R., Tomkins, A.: Evolutionary clustering. In: The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 554–560 (2006)
Handl, J., Knowles, J.: An evolutionary approach to multiobjective clustering. IEEE Trans. Evol. Comput. 11(1), 56–76 (2007)
Ripon, K.S.N., et al.: Multi-objective evolutionary clustering using variable-length real jumping genes genetic algorithm. In: The 18th International Conference on Pattern Recognition, pp. 1200–1203 (2006)
Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S.: A survey of multiobjective evolutionary clustering. ACM Comput. Surv. (CSUR) 47(4), 61 (2015)
Ben Said, L., Bechikh, S., Ghédira, K.: The r-dominance: a new dominance relation for interactive evolutionary multicriteria decision making. IEEE Trans. Evol. Comput. 14(5), 801–818 (2010)
Chi, Y., et al.: Evolutionary spectral clustering by incorporating temporal smoothness. In: The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 153–162 (2007)
Folino, F., Pizzuti, C.: A multiobjective and evolutionary clustering method for dynamic networks. In: The International Conference on Advances in Social Networks Analysis and Mining, pp. 256–263 (2010)
Folino, F., Pizzuti, C.: An evolutionary multiobjective approach for community discovery in dynamic networks. IEEE Trans. Knowl. Data Eng. 26(8), 1838–1852 (2014)
Ma, J., et al.: Decomposition-based multiobjective evolutionary algorithm for community detection in dynamic social networks. Sci. World J. 2014, 1–22 (2014)
Zhou, X., et al.: Multiobjective biogeography based optimization algorithm with decomposition for community detection in dynamic networks. Physica A 436, 430–442 (2015)
Chen, G., Wang, Y., Wei, J.: A new multiobjective evolutionary algorithm for community detection in dynamic complex networks. Math. Probl. Eng. 2013, 1–7 (2013)
Ma, J., et al.: Spatio-temporal data evolutionary clustering based on MOEA/D. In: The 13th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 85–86 (2011)
Chen, G., Luo, W., Zhu, T.: Evolutionary clustering with differential evolution. In: The 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1382–1389 (2014)
Deb, K., Goyal, M.: A combined genetic adaptive search (GeneAS) for engineering design. Comput. Sci. Inform. 26, 30–45 (1996)
Bandyopadhyay, S., Maulik, U.: An evolutionary technique based on K-means algorithm for optimal clustering in RN. Inf. Sci. 146(1), 221–237 (2002)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 1(2), 224–227 (1979)
Chen, W.-Y., et al.: Parallel spectral clustering in distributed systems. IEEE Trans. Pattern Anal. Mach. Intell. 33(3), 568–586 (2011)
Agrawal, S., Panigrahi, B., Tiwari, M.K.: Multiobjective particle swarm algorithm with fuzzy clustering for electrical power dispatch. IEEE Trans. Evol. Comput. 12(5), 529–541 (2008)
Deb, K., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Xu, K.S., Kliger, M., Hero III, A.O.: Adaptive evolutionary clustering. Data Min. Knowl. Disc. 28(2), 304–336 (2014)
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This work is partly supported by Anhui Provincial Natural Science Foundation (No. 1408085MKL07) and National Natural Science Foundation of China (No. 61573327).
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Gao, W., Luo, W., Bu, C., Ni, L., Zhang, D. (2016). Clustering Evolutionary Data with an r-Dominance Based Multi-objective Evolutionary Algorithm. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_37
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DOI: https://doi.org/10.1007/978-3-319-46257-8_37
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